Visualizing and Modeling 2016 US Presidential Election Results
This data setup stolen brazenly from Jennifer Thompson.
## Load libraries we'll use
# library(devtools)
# install_github('datadotworld/data.world-r')
library(data.world) ## for querying directly from data.world
library(tidyverse) ## for data wrangling and piping
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filter(): dplyr, stats
lag(): dplyr, stats
query(): dplyr, data.worldlibrary(rms) ## rms has nice functionality for getting predicted values from model objectsLoading required package: Hmisc
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smithsWe want to determine what county characteristics may be predictors of both the county’s final winner in the 2016 presidential election, and the margin of victory by which that candidate won.
Project Setup
Loading Data
We’ll load the county characteristics and 2016 presidential election results datasets directly from data.world.
County Characteristics
These data are mostly from the 2015 American Community Survey, with additional data from other sources. A full data dictionary can be found here.
library(data.world)
## Set connection (see package README for details: https://github.com/datadotworld/data.world-r)
conn<-data.world(read_csv('C:/Users/rkahne/Documents/data_world_api.csv')$key)
# ## What data tables are available? (both dplyr and data.world have a query(); must specify)
# data_list <- data.world::query(conn,
# dataset = 'data4democracy/election-transparency',
# query = "SELECT * FROM Tables")
# data_list
countyChar <- data.world::query(conn,
dataset = 'data4democracy/election-transparency',
query = "SELECT * FROM CountyCharacteristics")Voter Registration
We also want party registration data from November 2016, queried from the full PartyRegistration file. This file includes data pulled from each state’s Secretary of State web site. Full data dictionary is here.
Some of the variable names overlap with names in the next dataset; we’ll drop variables that are redundant (state/county names/abbreviations and year/month of registration) and add “Reg” to everything else except state/county keys to clarify that it’s registration info.
voterReg2016 <-
data.world::query(conn,
dataset = 'data4democracy/election-transparency',
query = "SELECT * FROM PartyRegistration WHERE Year = 2016 AND Month = 11")
voterReg2016 <- voterReg2016 %>%
select(-one_of("CountyName", "StateName", "StateAbbr", "Year", "Month", "YearMonth"))
names(voterReg2016) <- ifelse(names(voterReg2016) %in% c('State', 'County'), names(voterReg2016),
paste0(names(voterReg2016), 'Reg'))Presidential Election Results by County
These data are collected from a Harvard research project. A full data dictionary can be found here.
presResults2016 <- data.world::query(conn,
dataset = 'data4democracy/election-transparency',
query = "SELECT * FROM PresidentialElectionResults2016")Descriptive Statistics
Let’s join the datasets, calculate some proportions, and look at some basic descriptive statistics.
## Check what variables are in common
# intersect(names(countyChar), names(voterReg2016))
# intersect(names(countyChar), names(presResults2016))
data2016 <- reduce(list(countyChar, voterReg2016, presResults2016),
left_join,
by = c('County', 'State'))
## Function to quickly calculate a proportion out of TotalPopulation - we'll need to do this a lot
prop_total <- function(x){ x / data2016$TotalPopulation }
data2016 <- data2016 %>%
## Calculate lots of proportion variables
mutate(propMale = prop_total(Male),
propKids = prop_total(Age0_4 + Age5_9 + Age10_14 + Age15_19),
propAdultsNoTeens = 1 - propKids,
## 15-19 is included in labor force, marital status questions
totalAdultsWithTeens = Age15_19 + Age20_24 + Age25_34 + Age35_44 + Age45_54 + Age55_59 +
Age60_64 + Age65_74 + Age75_84 + Age85,
propAdultsWithTeens = prop_total(totalAdultsWithTeens),
## Only >18 included in education questions
totalAdultsNoTeens = Age20_24 + Age25_34 + Age35_44 + Age45_54 + Age55_59 + Age60_64 +
Age65_74 + Age75_84 + Age85,
propElders = prop_total(Age65_74 + Age75_84 + Age85),
propNMarried = NeverMarried / totalAdultsWithTeens,
propHispanic = prop_total(Hispanic),
propWhite = prop_total(White),
propBlack = prop_total(Black),
majWhite = propWhite > 0.5,
majBlack = propBlack > 0.5,
propNoHS = (EdK8 + Ed9_12) / totalAdultsNoTeens,
propHS = EdHS / totalAdultsNoTeens,
propMoreHS = (EdCollNoDegree + EdAssocDegree + EdBachelorDegree + EdGraduateDegree) /
totalAdultsNoTeens,
propMfg2015 = MfgEmp2015 / LaborForce,
propUnemp = Unemployment / LaborForce,
propLaborForce = prop_total(LaborForce),
propStein = stein / totalvotes,
propJohnson = johnson / totalvotes,
propTrump = trump / totalvotes,
propClinton = clinton / totalvotes,
propVoters = totalvotes / totalAdultsNoTeens,
votedTrump = rPct > 0.5,
state_EV = electoral_votes[StateName])
## View full data frame
data2016Visualizations!
- I made some small modifications to the code above, my modficiations mostly turned out to be useless, anyway.
- I (rkahne) really started doing work around here.
- The goal (for me, right now) is to build out dataframes from the great work done by Jennifer Thompson that can be used with ggplot.
- I’ve done some Very Basic Barplots to get started. I hope other people can take this stuff, modify it, and find some cool and interesting stuff in here.
Candidate vote totals by State
vote_by_state<-select(data2016, StateName, state_EV, clinton, trump, johnson, stein, other) %>%
melt(id= c('StateName','state_EV')) %>%
group_by(StateName, state_EV, variable) %>%
dplyr::summarize(total_vote = sum(value)) %>% # Dunno how plyr got in, but whatever.
complete(variable) %>%
ungroup() %>%
group_by(StateName) %>%
mutate(sum_total = sum(total_vote, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_vote),NA,paste0(round(total_vote / sum_total, 4)*100,'%')),
proportion_numeric = total_vote / sum_total) %>%
select(-sum_total)
candidate_order <- 'clinton' # CHANGE ME
level_order<-(filter(vote_by_state,variable == candidate_order) %>%
select(StateName, proportion_numeric) %>%
arrange(desc(proportion_numeric)))$StateName
vote_by_state$StateName<- factor(vote_by_state$StateName, levels = level_order)
vote_by_state$variable<-factor(vote_by_state$variable, levels = c('other','stein','johnson','trump','clinton'))
ggplot(vote_by_state, aes(x=StateName, y=total_vote, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('clinton' = 'blue', 'trump' = 'red', 'stein' = 'green','johnson' = 'yellow','other' = 'purple'),
name = 'Candidate',
breaks = c('other','stein','johnson','trump','clinton'),
labels = c('Others','Jill Stein','Gary Johnson','Donald J Trump','Hillary Clinton')) +
labs(x = NULL, y = 'Percent of Vote')+
scale_y_continuous(labels = scales::percent) +
ggtitle('Percent of Vote by State', subtitle='2016 USA Presidential Election') +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Candidate Vote Totals by County Within State
Feel free to play with the variable in this chunk.
state<-'Tennessee'
vote_by_county<-filter(data2016, StateName == state) %>%
select(CountyName, clinton, trump, johnson, stein, other) %>%
melt(id= 'CountyName') %>%
group_by(CountyName, variable) %>%
dplyr::summarize(total_vote = sum(value)) %>% # Dunno how plyr got in, but whatever.
complete(variable) %>%
ungroup() %>%
group_by(CountyName) %>%
mutate(sum_total = sum(total_vote, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_vote),NA,paste0(round(total_vote / sum_total, 4)*100,'%')),
proportion_numeric = total_vote / sum_total) %>%
select(-sum_total)
candidate_order <- 'clinton' # CHANGE ME
level_order<-(filter(vote_by_county,variable == candidate_order) %>%
select(CountyName, proportion_numeric) %>%
arrange(desc(proportion_numeric)))$CountyName
vote_by_county$CountyName<- factor(vote_by_county$CountyName, levels = level_order)
vote_by_county$variable<-factor(vote_by_county$variable, levels = c('other','stein','johnson','trump','clinton'))
ggplot(vote_by_county, aes(x=CountyName, y=total_vote, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('clinton' = 'blue', 'trump' = 'red', 'stein' = 'green','johnson' = 'yellow','other' = 'purple'),
name = 'Candidate',
breaks = c('other','stein','johnson','trump','clinton'),
labels = c('Others','Jill Stein','Gary Johnson','Donald J Trump','Hillary Clinton')) +
labs(x = NULL, y = 'Percent of Vote')+
scale_y_continuous(labels = scales::percent) +
ggtitle(paste0('Percent of Vote by County - ',state), subtitle='2016 USA Presidential Election') +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Ternary Plot
- Let’s try something crazy?!
- Here’s an interesting idea for looking at the county by county results.
- Utah had a high vote total for Evan McMillin, and this is a way to visualize Utah’s uniqueness.
library(ggtern)
ternary_vote<-mutate(data2016, third_party = johnson + stein + other) %>%
select(County, StateName, clinton, trump, third_party) %>%
melt(id= c('County','StateName')) %>%
group_by(County, StateName, variable) %>%
dplyr::summarize(total_vote = sum(value)) %>% # Dunno how plyr got in, but whatever.
complete(variable) %>%
ungroup() %>%
group_by(County) %>%
mutate(sum_total = sum(total_vote, na.rm=T)) %>%
mutate(proportion_numeric = total_vote / sum_total) %>%
select(-sum_total, -total_vote) %>%
spread(key = variable, value = proportion_numeric)
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ggtern(ternary_vote, aes(x=clinton, y=trump, z=third_party, color=StateName))+
geom_point(aes(alpha = 0.2))
Party Registration by State
Not all states have partisan registration.
party_registration <- select(voterReg2016, State, DReg, RReg, OReg, GReg, LReg, NReg) %>%
melt(id= 'State') %>%
group_by(State, variable) %>%
dplyr::summarize(total_reg = sum(value)) %>%
complete(variable) %>%
ungroup() %>%
group_by(State) %>%
mutate(sum_total = sum(total_reg, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_reg),NA,paste0(round(total_reg / sum_total, 4)*100,'%')),
proportion_numeric = total_reg / sum_total) %>%
select(-sum_total)
party_registration$State<-sapply(party_registration$State, function(i) names(state_names)[which(state_names == i)])
non_partisan_states<-(filter(party_registration, variable == 'NReg') %>%
filter(proportion_numeric == 1))$State
party_registration <- filter(party_registration, !(State %in% non_partisan_states))
party_order <- 'DReg' # CHANGE ME
level_order<-(filter(party_registration,variable == party_order) %>%
select(State, proportion_numeric) %>%
arrange(desc(proportion_numeric)))$State
party_registration$State<- factor(party_registration$State, levels = level_order)
party_registration$variable<-factor(party_registration$variable, levels = c('NReg','OReg','GReg','LReg','RReg','DReg'))
ggplot(party_registration, aes(x=State, y=total_reg, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('DReg' = 'blue', 'RReg' = 'red', 'GReg' = 'green','LReg' = 'yellow','OReg' = 'purple','NReg' = 'grey'),
name = 'Candidate',
breaks = c('NReg','OReg','GReg','LReg','RReg','DReg'),
labels = c('No Party','Other Party','Green','Libertarian','Republican','Democratic')) +
labs(x = NULL, y = 'Percent of Registered Voters')+
scale_y_continuous(labels = scales::percent) +
ggtitle('Percent of Voter Registration by State', subtitle='2016') +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Looks like we have a bit of an issue here with Other and Non-partisan registration. NE and WV are all “Other”.
Partisan Registration by County within State
Feel free to play with the variable in this.
state <- 'West Virginia'
party_registration_c <- filter(data2016, StateName == state) %>%
select(CountyName, DReg, RReg, OReg, GReg, LReg, NReg) %>%
melt(id= 'CountyName') %>%
group_by(CountyName, variable) %>%
dplyr::summarize(total_reg = sum(value)) %>%
complete(variable) %>%
ungroup() %>%
group_by(CountyName) %>%
mutate(sum_total = sum(total_reg, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_reg),NA,paste0(round(total_reg / sum_total, 4)*100,'%')),
proportion_numeric = total_reg / sum_total) %>%
select(-sum_total)
party_order <- 'DReg' # CHANGE ME
level_order<-(filter(party_registration_c,variable == party_order) %>%
select(CountyName, proportion_numeric) %>%
arrange(desc(proportion_numeric)))$CountyName
party_registration_c$CountyName<- factor(party_registration_c$CountyName, levels = level_order)
party_registration_c$variable<-factor(party_registration_c$variable, levels = c('NReg','OReg','GReg','LReg','RReg','DReg'))
ggplot(party_registration_c, aes(x=CountyName, y=total_reg, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('DReg' = 'blue', 'RReg' = 'red', 'GReg' = 'green','LReg' = 'yellow','OReg' = 'purple','NReg' = 'grey'),
name = 'Candidate',
breaks = c('NReg','OReg','GReg','LReg','RReg','DReg'),
labels = c('No Party','Other Party','Green','Libertarian','Republican','Democratic')) +
labs(x = NULL, y = 'Percent of Registered Voters')+
scale_y_continuous(labels = scales::percent) +
ggtitle('Percent of Voter Registration by County', subtitle=paste0(state,' - 2016')) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Voter Turnout by State
- We don’t have great data to do this, becuase the census bracket is from age 15-19, which cuts out two years of voting age.
- Maybe because of this (but maybe not!) there are some states where (voted+registered but didn’t vote) > total voting population.
- Even if the issue is not the census populations, it could be merely out of date voter registration data.
turnout_data <- group_by(data2016, StateName) %>%
dplyr::summarize(total_votes = sum(clinton,trump,johnson,stein,other, na.rm=T),
total_reg_no_vote = sum(DReg,RReg,OReg,GReg,LReg,NReg, na.rm=T)-total_votes,
total_not_registered = ifelse(sum(TotalPopulation)- sum(Age0_4)- sum(Age5_9)- sum(Age10_14) - sum(Age15_19) -
total_reg_no_vote - total_votes<0,NA,sum(TotalPopulation)- sum(Age0_4)- sum(Age5_9)- sum(Age10_14) -
sum(Age15_19) - total_reg_no_vote - total_votes)) %>%
melt(id = 'StateName') %>%
group_by(StateName, variable) %>%
dplyr::summarize(total_pop = sum(value)) %>%
complete(variable) %>%
ungroup() %>%
group_by(StateName) %>%
mutate(sum_total = sum(total_pop, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_pop),NA,paste0(round(total_pop / sum_total, 4)*100,'%')),
proportion_numeric = total_pop / sum_total) %>%
select(-sum_total)
turnout_data$variable<-factor(turnout_data$variable, levels = c('total_not_registered','total_reg_no_vote','total_votes'))
ggplot(turnout_data, aes(x=StateName, y=total_pop, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('total_votes' = 'green', 'total_reg_no_vote' = 'purple', 'total_not_registered' = 'grey'),
name = 'Population',
breaks = c('total_votes','total_reg_no_vote','total_not_registered'),
labels = c('Voted','Registered, but did not vote','Not Registered')) +
labs(x = NULL, y = 'Percent of Population')+
scale_y_continuous(labels = scales::percent) +
ggtitle('Voter Turnout by State', subtitle='2016') +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Voter Turnout by County Within State
state <- 'Kentucky'
turnout_data_c <- filter(data2016, StateName == state) %>%
group_by(CountyName) %>%
dplyr::summarize(total_votes = sum(clinton,trump,johnson,stein,other, na.rm=T),
total_reg_no_vote = sum(DReg,RReg,OReg,GReg,LReg,NReg, na.rm=T)-total_votes,
total_not_registered = ifelse(sum(TotalPopulation)- sum(Age0_4)- sum(Age5_9)- sum(Age10_14) - sum(Age15_19) -
total_reg_no_vote - total_votes<0,NA,sum(TotalPopulation)- sum(Age0_4)- sum(Age5_9)- sum(Age10_14) -
sum(Age15_19) - total_reg_no_vote - total_votes)) %>%
melt(id = 'CountyName') %>%
group_by(CountyName, variable) %>%
dplyr::summarize(total_pop = sum(value)) %>%
complete(variable) %>%
ungroup() %>%
group_by(CountyName) %>%
mutate(sum_total = sum(total_pop, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_pop),NA,paste0(round(total_pop / sum_total, 4)*100,'%')),
proportion_numeric = total_pop / sum_total) %>%
select(-sum_total)
turnout_data_c$variable<-factor(turnout_data_c$variable, levels = c('total_not_registered','total_reg_no_vote','total_votes'))
ggplot(turnout_data_c, aes(x=CountyName, y=total_pop, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('total_votes' = 'green', 'total_reg_no_vote' = 'purple', 'total_not_registered' = 'grey'),
name = 'Population',
breaks = c('total_votes','total_reg_no_vote','total_not_registered'),
labels = c('Voted','Registered, but did not vote','Not Registered')) +
labs(x = NULL, y = 'Percent of Population')+
scale_y_continuous(labels = scales::percent) +
ggtitle('Voter Turnout by County', subtitle=paste0(state,', 2016')) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Victory Margins
- Not quite sure what we are going for, here.
Demographic / Economic Variables
- Let’s look at % of vote faceted over some different variables
- This graphic produces county by county results facetting by quintile of Demographic/Economic variable
- You can change the variable in the code to see other variables.
- There is one categorical variable, the code for producing it’s facetted plot is in commented in this chunk.
Demo_Factors <- select(data2016, County, rDRPct, NCHS_UrbanRural2013, TotalPopulation, MedianAge, MedianHouseholdIncome, SimpsonDiversityIndex, propWhite, propBlack, propMfg2015, propHS, propMoreHS, propUnemp, propElders, propNMarried)
lm(rDRPct~.,select(Demo_Factors,-County)) %>% summary()
Call:
lm(formula = rDRPct ~ ., data = select(Demo_Factors, -County))
Residuals:
Min 1Q Median 3Q Max
-0.60295 -0.04971 0.00257 0.05506 0.34930
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.032e+00 5.994e-02 17.218 < 2e-16 ***
NCHS_UrbanRural2013Large fringe metro 2.919e-02 1.512e-02 1.931 0.053553 .
NCHS_UrbanRural2013Medium metro 1.546e-02 1.505e-02 1.027 0.304418
NCHS_UrbanRural2013Micropolitan (nonmetropolitan) 3.160e-02 1.549e-02 2.040 0.041463 *
NCHS_UrbanRural2013Noncore (nonmetropolitan) 5.146e-02 1.566e-02 3.286 0.001031 **
NCHS_UrbanRural2013Small metro 3.236e-02 1.558e-02 2.077 0.037899 *
TotalPopulation -4.223e-08 6.863e-09 -6.154 8.68e-10 ***
MedianAge -1.429e-02 9.947e-04 -14.370 < 2e-16 ***
MedianHouseholdIncome -6.213e-07 2.608e-07 -2.382 0.017284 *
SimpsonDiversityIndex 2.227e-01 2.577e-02 8.641 < 2e-16 ***
propWhite 5.219e-01 3.796e-02 13.748 < 2e-16 ***
propBlack 9.281e-02 2.814e-02 3.298 0.000987 ***
propMfg2015 -2.879e-04 5.838e-04 -0.493 0.621911
propHS 3.440e-01 5.481e-02 6.275 4.04e-10 ***
propMoreHS -2.194e-01 4.255e-02 -5.156 2.70e-07 ***
propUnemp 3.636e-05 5.378e-04 0.068 0.946102
propElders 5.882e-01 1.140e-01 5.159 2.66e-07 ***
propNMarried -1.490e+00 4.423e-02 -33.682 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0898 on 2736 degrees of freedom
(387 observations deleted due to missingness)
Multiple R-squared: 0.6721, Adjusted R-squared: 0.6701
F-statistic: 329.9 on 17 and 2736 DF, p-value: < 2.2e-16Demo_Factors <- select(data2016, County, trump, clinton, johnson, stein, other, NCHS_UrbanRural2013, TotalPopulation, MedianAge, MedianHouseholdIncome, SimpsonDiversityIndex, propWhite, propBlack, propMfg2015, propHS, propMoreHS, propUnemp, propElders, propNMarried)
vote_by_county_f<-select(Demo_Factors, County, clinton, trump, johnson, stein, other) %>%
melt(id= c('County')) %>%
group_by(County, variable) %>%
dplyr::summarize(total_vote = sum(value)) %>% # Dunno how plyr got in, but whatever.
complete(variable) %>%
ungroup() %>%
group_by(County) %>%
mutate(sum_total = sum(total_vote, na.rm=T)) %>%
mutate(proportion = ifelse(is.na(total_vote),NA,paste0(round(total_vote / sum_total, 4)*100,'%')),
proportion_numeric = total_vote / sum_total) %>%
select(-sum_total)
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|=============================================================================================================================== | 99% ~0 s remaining vote_by_county_f <- left_join(vote_by_county_f, Demo_Factors, by='County')
candidate_order <- 'clinton' # CHANGE ME
level_order<-(filter(vote_by_county_f,variable == candidate_order) %>%
select(County, proportion_numeric) %>%
arrange(desc(proportion_numeric)))$County
vote_by_county_f$County<- factor(vote_by_county_f$County, levels = level_order)
vote_by_county_f$variable<-factor(vote_by_county_f$variable, levels = c('other','stein','johnson','trump','clinton'))
# ggplot(filter(vote_by_county_f, !is.na(NCHS_UrbanRural2013)), aes(x=County, y=total_vote, fill=variable)) +
# geom_bar(stat='identity', position = 'fill') +
# scale_fill_manual(values = c('clinton' = 'blue', 'trump' = 'red', 'stein' = 'green','johnson' = 'yellow','other' = 'purple'),
# name = 'Candidate',
# breaks = c('other','stein','johnson','trump','clinton'),
# labels = c('Others','Jill Stein','Gary Johnson','Donald J Trump','Hillary Clinton')) +
# labs(x = NULL, y = 'Percent of Vote')+
# scale_y_continuous(labels = scales::percent) +
# ggtitle('Percent of Vote by County', subtitle='2016 USA Presidential Election') +
# theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
# facet_wrap(~NCHS_UrbanRural2013, scales = 'free_x')
facetting_variable <- 'MedianAge' #Change Me
vote_by_county_f$fct <- cut(vote_by_county_f[[facetting_variable]],
breaks = c(0,quantile(vote_by_county_f[[facetting_variable]],
probs = c(0.1, 0.25, 0.5, 0.75, 0.9),
na.rm = T),Inf),
ordered_result = T,
labels = c('Lowest 10%','10th - 25th %ile',
'25th %ile - Median','Median - 75th %ile',
'75th - 90th %ile', 'Top 10%'))
ggplot(filter(vote_by_county_f, !is.na(fct)), aes(x=County, y=total_vote, fill=variable)) +
geom_bar(stat='identity', position = 'fill') +
scale_fill_manual(values = c('clinton' = 'blue', 'trump' = 'red', 'stein' = 'green','johnson' = 'yellow','other' = 'purple'),
name = 'Candidate',
breaks = c('other','stein','johnson','trump','clinton'),
labels = c('Others','Jill Stein','Gary Johnson','Donald J Trump','Hillary Clinton')) +
labs(x = NULL, y = 'Percent of Vote')+
scale_y_continuous(labels = scales::percent) +
ggtitle(paste0('Percent of Vote by County ~ ',facetting_variable), subtitle='2016 USA Presidential Election') +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
facet_wrap(~fct, scales = 'free_x')
- Let’s try different ways of facetting.
- Here we are looking at the impact of different variables on their vote for Trump. I’ve also included a df for individual r-squared values, here.
states_by_region <- read_csv('./states_by_region.csv')
data2016 <- left_join(data2016, select(states_by_region, -`State Code`), by = c('StateName'='State'))
Demo_Factors <- mutate(data2016, MfgEmpChange1980_2015 = (MfgEmp2015/TotalEmp2015) - (MfgEmp1980/TotalEmp1980)) %>%
select(County, rDRPct, Region, MfgEmpChange1980_2015, NCHS_UrbanRural2013, TotalPopulation, MedianAge, MedianHouseholdIncome, SimpsonDiversityIndex, propWhite, propBlack, propMfg2015, propHS, propMoreHS, propUnemp, propElders, propNMarried)
Demo_Factors$Region<- as.factor(Demo_Factors$Region)
Demo_Factors_m <- melt(select(Demo_Factors, -NCHS_UrbanRural2013), id = c('County','rDRPct'))
r_squareds <- data_frame(variable = unique(Demo_Factors_m$variable))
r_squareds$r_square <- sapply(r_squareds$variable, function(i){
percent_format()(summary(lm(unlist(Demo_Factors[as.character(i)])~Demo_Factors$rDRPct))$r.squared)
})
Demo_Factors_m <- left_join(Demo_Factors_m, r_squareds, by='variable')
ggplot(Demo_Factors_m, aes(x=value, y=rDRPct)) +
geom_point() +
geom_smooth(method = 'lm') +
facet_wrap(~variable, scales = 'free_x')
Manufacturing Visualization
# pulled from https://github.com/cphalpert/census-regions/blob/master/us%20census%20bureau%20regions%20and%20divisions.csv
states_by_region <- read_csv('./states_by_region.csv')
data2016 <- left_join(data2016, select(states_by_region, -`State Code`), by = c('StateName'='State'))
Demo_Factors <- mutate(data2016, MfgEmpChange1980_2015 = (MfgEmp2015/TotalEmp2015) - (MfgEmp1980/TotalEmp1980),
region_division = paste(Region,'-',Division)) %>%
select(County, rDRPct, Region, region_division, MfgEmpChange1980_2015, NCHS_UrbanRural2013, TotalPopulation, MedianAge,
MedianHouseholdIncome, SimpsonDiversityIndex, propWhite, propBlack, propMfg2015, propHS, propMoreHS, propUnemp,
propElders, propNMarried)
Demo_Factors$Region<- as.factor(Demo_Factors$Region)
Demo_Factors$region_division <- as.factor(Demo_Factors$region_division)
Demo_Factors_m2 <- melt(select(Demo_Factors, County, rDRPct, MfgEmpChange1980_2015, propWhite, region_division),
id = c('County','rDRPct','MfgEmpChange1980_2015'))
regional_mfg<-filter(Demo_Factors_m2, variable == 'region_division', !is.na(MfgEmpChange1980_2015)) %>% select(-variable)
ggplot(regional_mfg, aes(x=MfgEmpChange1980_2015, y=rDRPct))+
geom_point()+
geom_smooth(method = 'lm')+
facet_wrap(~value)
ggplot(regional_mfg, aes(x=value, y=MfgEmpChange1980_2015)) +
geom_boxplot()+
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Demo_Factors<-left_join(Demo_Factors, mutate(data2016, BlindDisabledSSI_p = BlindDisabledSSI / TotalPopulation)
%>% select(County, BlindDisabledSSI_p))Models
summary(lm(rDRPct ~ Region + propWhite + NCHS_UrbanRural2013 + propNMarried, Demo_Factors))
Call:
lm(formula = rDRPct ~ Region + propWhite + NCHS_UrbanRural2013 +
propNMarried, data = Demo_Factors)
Residuals:
Min 1Q Median 3Q Max
-0.59819 -0.05725 0.01011 0.06696 0.37174
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.440370 0.026992 16.315 < 2e-16 ***
RegionNortheast -0.066455 0.007889 -8.424 < 2e-16 ***
RegionSouth 0.063649 0.004628 13.752 < 2e-16 ***
RegionWest -0.024467 0.005854 -4.180 3.00e-05 ***
propWhite 0.373048 0.016642 22.416 < 2e-16 ***
NCHS_UrbanRural2013Large fringe metro 0.082674 0.013900 5.948 3.02e-09 ***
NCHS_UrbanRural2013Medium metro 0.096519 0.013858 6.965 3.99e-12 ***
NCHS_UrbanRural2013Micropolitan (nonmetropolitan) 0.141172 0.013474 10.477 < 2e-16 ***
NCHS_UrbanRural2013Noncore (nonmetropolitan) 0.160562 0.013363 12.016 < 2e-16 ***
NCHS_UrbanRural2013Small metro 0.129140 0.013907 9.286 < 2e-16 ***
propNMarried -0.906555 0.038321 -23.657 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1023 on 3128 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.6021, Adjusted R-squared: 0.6008
F-statistic: 473.3 on 10 and 3128 DF, p-value: < 2.2e-16Manufacturing Decline
- Where? Within region?
BLS Data
- I pulled in labor force data from BLS, and joined it to our dataset.
- The number of discouraged workers by county was not available, so I tried to back into the number.
- There are significant problems with the way I did this, because it doesn’t account for school or disability or anything else.
- It turns out it’s not very explanatory.
summary(lm(rDRPct ~ BlindDisabledSSI_p + propDiscouraged + MfgEmpChange1980_2015, Demo_Factors))
Call:
lm(formula = rDRPct ~ BlindDisabledSSI_p + propDiscouraged +
MfgEmpChange1980_2015, data = Demo_Factors)
Residuals:
Min 1Q Median 3Q Max
-0.59099 -0.08812 0.02904 0.11475 0.32123
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.661338 0.006841 96.679 < 2e-16 ***
BlindDisabledSSI_p -1.027191 0.253088 -4.059 5.09e-05 ***
propDiscouraged 0.241958 0.056277 4.299 1.78e-05 ***
MfgEmpChange1980_2015 0.147426 0.036760 4.010 6.25e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1537 on 2396 degrees of freedom
(741 observations deleted due to missingness)
Multiple R-squared: 0.01473, Adjusted R-squared: 0.0135
F-statistic: 11.94 on 3 and 2396 DF, p-value: 9.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